Results 1 to 10 of about 21,157 (187)

Uncertainty Prediction for Monocular 3D Object Detection [PDF]

Sensors, 2023
For object detection, capturing the scale of uncertainty is as important as accurate localization. Without understanding uncertainties, self-driving vehicles cannot plan a safe path. Many studies have focused on improving object detection, but relatively
Junghwan Mun, Hyukdoo Choi
doaj   +4 more sources

MonoDCN: Monocular 3D object detection based on dynamic convolution. [PDF]

PLoS ONE, 2022
3D object detection is vital in the environment perception of autonomous driving. The current monocular 3D object detection technology mainly uses RGB images and pseudo radar point clouds as input.
Shenming Qu, Xinyu Yang, Yiming Gao, Shengbin Liang   +3 more
doaj   +5 more sources

GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution [PDF]

PeerJ Computer Science, 2021
Monocular 3D object detection has recently become prevalent in autonomous driving and navigation applications due to its cost-efficiency and easy-to-embed to existent vehicles.
Minh-Quan Viet Bui, Duc Tuan Ngo, Hoang-Anh Pham, Duc Dung Nguyen   +3 more
doaj   +3 more sources

MonoDFNet: Monocular 3D Object Detection with Depth Fusion and Adaptive Optimization [PDF]

Sensors
Monocular 3D object detection refers to detecting 3D objects using a single camera. This approach offers low sensor costs, high resolution, and rich texture information, making it widely adopted.
Yuhan Gao, Peng Wang, Xiaoyan Li, Mengyu Sun, Ruohai Di, Liangliang Li, Wei Hong   +6 more
doaj   +2 more sources

Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information [PDF]

Sensors, 2022
Recently, the research on monocular 3D target detection based on pseudo-LiDAR data has made some progress. In contrast to LiDAR-based algorithms, the robustness of pseudo-LiDAR methods is still inferior. After conducting in-depth experiments, we realized
Henan Hu, Ming Zhu, Muyu Li, Kwok-Leung Chan   +3 more
doaj   +2 more sources

Applying auxiliary supervised depth-assisted transformer and cross modal attention fusion in monocular 3D object detection [PDF]

PeerJ Computer Science
Monocular 3D object detection is the most widely applied and challenging solution for autonomous driving, due to 2D images lacking 3D information. Existing methods are limited by inaccurate depth estimations by inequivalent supervised targets. The use of
Zhijian Wang, Jie Liu, Yixiao Sun, Xiang Zhou, Boyan Sun, Dehong Kong, Jay Xu, Xiaoping Yue, Wenyu Zhang   +8 more
doaj   +3 more sources

MonoAux: Fully Exploiting Auxiliary Information and Uncertainty for Monocular 3D Object Detection [PDF]

Cyborg and Bionic Systems
Monocular 3D object detection plays a pivotal role in autonomous driving, presenting a formidable challenge by requiring the precise localization of 3D objects within a single image, devoid of depth information.
Zhenglin Li, Wenbo Zheng, Le Yang, Liyan Ma, Yang Zhou, Yan Peng   +5 more
doaj   +2 more sources

Competition for roadside camera monocular 3D object detection. [PDF]

Natl Sci Rev, 2023
Jia J, Shi Y, Qu Y, Wang R, Xu X, Zhang H.   +5 more
europepmc   +3 more sources

A Survey of Deep Learning-Based 3D Object Detection Methods for Autonomous Driving Across Different Sensor Modalities [PDF]

Sensors
This paper presents a comprehensive survey of deep learning-based methods for 3D object detection in autonomous driving, focusing on their use of diverse sensor modalities, including monocular cameras, stereo vision, LiDAR, radar, and multi-modal fusion.
Miguel Valverde, Alexandra Moutinho, João-Vitor Zacchi   +2 more
doaj   +2 more sources

Metric scale non-fixed obstacles distance estimation using a 3D map and a monocular camera [PDF]

Frontiers in Robotics and AI
Obstacle avoidance is important for autonomous driving. Metric scale obstacle detection using a monocular camera for obstacle avoidance has been studied.
Daijiro Higashi, Naoki Fukuta, Tsuyoshi Tasaki   +2 more
doaj   +2 more sources